Capillary Isoelectric Focusing with Laser-Induced Fluorescence Whole Column Imaging Detection as a Tool To Monitor Reactions of Proteins
Bibliographic record
Abstract
Capillary isoelectric focusing (CIEF) with laser-induced fluorescence (LIF) whole column imaging detection (WCID) has the characteristics of high resolution, high speed and high sensitivity for separation of amphoteric biomolecules. These features enable a CIEF-LIF-WCID system to monitor the dynamic process of a protein reaction. The reaction can be a physical change or a chemical reaction, provided that the kinetics of the reaction is slower than the focusing speed or that the intermediates involved have long enough life-span compared to the analysis time. The processes of denaturation (a physical reaction), reduction and carbamylation (both chemical reactions) were dynamically monitored. The CIEF profiles at successive reaction times clearly displayed the formation of different products at different stages. At incomplete denaturation, intermediates with higher apparent pI values relative to the products at complete denaturation were detected. Carbamylation products of a protein were detected when the protein reacted with a urea solution that had prepared three months earlier, exhibiting gradually decreased pI values. Mechanisms involved in these reactions were rationalized. A combined mechanism of denaturation and reduction was suggested to explain the denaturing process under high concentrations of urea. Potential applications and critical factors to manipulate these reactions were also discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".